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Robust Entangled-Photon Ghost Imaging with Compressive Sensing

This work experimentally demonstrates that the imaging quality of quantum ghost imaging (GI) with entangled photons can be significantly improved by properly handling the errors caused by the imperfection of optical devices. We also consider compressive GI to reduce the number of measurements and th...

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Detalles Bibliográficos
Autores principales: Li, Jun, Gao, Wenyu, Qian, Jiachuan, Guo, Qinghua, Xi, Jiangtao, Ritz, Christian H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339165/
https://www.ncbi.nlm.nih.gov/pubmed/30621087
http://dx.doi.org/10.3390/s19010192
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author Li, Jun
Gao, Wenyu
Qian, Jiachuan
Guo, Qinghua
Xi, Jiangtao
Ritz, Christian H.
author_facet Li, Jun
Gao, Wenyu
Qian, Jiachuan
Guo, Qinghua
Xi, Jiangtao
Ritz, Christian H.
author_sort Li, Jun
collection PubMed
description This work experimentally demonstrates that the imaging quality of quantum ghost imaging (GI) with entangled photons can be significantly improved by properly handling the errors caused by the imperfection of optical devices. We also consider compressive GI to reduce the number of measurements and thereby the data acquisition time. The image reconstruction is formulated as a sparse total least square problem which is solved with an iterative algorithm. Our experiments show that, compared with existing methods, the new method can achieve a significant performance gain in terms of mean square error and peak signal–noise ratio.
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spelling pubmed-63391652019-01-23 Robust Entangled-Photon Ghost Imaging with Compressive Sensing Li, Jun Gao, Wenyu Qian, Jiachuan Guo, Qinghua Xi, Jiangtao Ritz, Christian H. Sensors (Basel) Article This work experimentally demonstrates that the imaging quality of quantum ghost imaging (GI) with entangled photons can be significantly improved by properly handling the errors caused by the imperfection of optical devices. We also consider compressive GI to reduce the number of measurements and thereby the data acquisition time. The image reconstruction is formulated as a sparse total least square problem which is solved with an iterative algorithm. Our experiments show that, compared with existing methods, the new method can achieve a significant performance gain in terms of mean square error and peak signal–noise ratio. MDPI 2019-01-07 /pmc/articles/PMC6339165/ /pubmed/30621087 http://dx.doi.org/10.3390/s19010192 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Jun
Gao, Wenyu
Qian, Jiachuan
Guo, Qinghua
Xi, Jiangtao
Ritz, Christian H.
Robust Entangled-Photon Ghost Imaging with Compressive Sensing
title Robust Entangled-Photon Ghost Imaging with Compressive Sensing
title_full Robust Entangled-Photon Ghost Imaging with Compressive Sensing
title_fullStr Robust Entangled-Photon Ghost Imaging with Compressive Sensing
title_full_unstemmed Robust Entangled-Photon Ghost Imaging with Compressive Sensing
title_short Robust Entangled-Photon Ghost Imaging with Compressive Sensing
title_sort robust entangled-photon ghost imaging with compressive sensing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339165/
https://www.ncbi.nlm.nih.gov/pubmed/30621087
http://dx.doi.org/10.3390/s19010192
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AT qianjiachuan robustentangledphotonghostimagingwithcompressivesensing
AT guoqinghua robustentangledphotonghostimagingwithcompressivesensing
AT xijiangtao robustentangledphotonghostimagingwithcompressivesensing
AT ritzchristianh robustentangledphotonghostimagingwithcompressivesensing